In this paper we present a novel distance measure, the minimum landscape distance (MLD). MLD provides a non-linear mapping between the elements in one sequence to those of another. Each element in one sequence is mapped to that with the highest neighbourhood structural similarity (landscape) in the other sequence within a search window. Experimental results obtained on sequences representing binary images show that MLD is superior to the Euclidean, correlation, and dynamic time warping (DTW) distance measures. We experimented with various sequence, landscape, and search window sizes.

In this paper, we present a novel approach to shape representation and description based on the combination of the Hilbert space filling curve and wavelet analysis. Our objective is to capitalize on the localization-preserving nature of the Hilbert space filling curve and the approximation power of the wavelet transform. The object image is scanned using the Hilbert curve and the resulting vector is smoothed using the wavelet transform and sampled. The technique is O(N) for both representation and comparison. We present some experimental results on the MPEG-7 dataset, Kimia-99 dataset, ETH-80 dataset, and a logo dataset.

In this paper, new wavelet-based affine invariant functions for shape representation are presented. Unlike the previous representation functions, only the approximation coefficients are used to obtain the proposed functions. One of the derived functions is computed by applying a single wavelet transform; the other function is calculated by applying two different wavelet transforms with two different wavelet families. One drawback of the previously derived detail-based invariant representation functions is that they are sensitive to noise at the finer scale levels, which limits the number of scale levels that can be used. The experimental results in this paper demonstrate that the proposed functions are more stable and less sensitive to noise than the detail-based functions.

Summary form only given. Location tracking systems are discrete in nature location information about each moving object (MO) is sampled at certain points in time. To determine the location of a MO between location reports or sometime in the future, we have to estimate the location at that point in time using the location reports we already have. The sampling frequency could affect the system's estimation accuracy as well as operating costs. Poor estimation accuracy could also carry a cost. The objective is to maximize the estimation accuracy while minimizing the operating cost. In this paper, we introduce the novel idea of using user modeling to improve the estimation accuracy of both the route and speed of a MO without the need to increase the sampling rate. We focus on a subset of moving objects we call roving users, or RUs for short. A RU is a human or human-controlled MO. The idea is that humans are creatures of habit. Knowing how a RU behaved in the past could help us estimate what he/she will do in the future. This knowledge could help us estimate the user location more accurately.

In this paper a barcode-based system to help the visually impaired identity objects in the environment and navigate through unknown territories is introduced. The system is based on the idea of tagging the different objects with 2D barcodes. With the aid of a portable camera and a computing device, the system can recognize and relay the barcode content to the user. We present the advantages of the proposed system compared to those of existing technologies. The different steps for recognizing and extracting the barcodes are described and applied to a sample image.

In this paper, a new 2D shape multiscale triangle-area representation (MTAR) is proposed. This representation utilizes a simple geometric principle, the area of a triangle, in obtaining a robust and efficient shape representation. The use of the wavelet transform for decomposing the boundary of the shapes improves the efficiency and robustness of the representation. The MTAR is more robust to the affine transformation, less affected by noise, and more selective than similar methods, e.g., the curvature scale-space CSS. Two tests, using MPEG-7 CE-shape-1 database, show that MTAR achieves better performance than the CSS under affine transformation and in the general shape retrieval.

In this paper, an algorithm for matching and classifying 2-D shapes that undergo affine transformation is developed. The algorithm uses the 1-D dyadic wavelet transform (DWT) to decompose a shape's boundary into multiscale levels. The curve moment invariants of the approximation coefficients are used as the shape features. Two different dissimilarities are calculated from the Euclidean distances between the decomposed scale levels of the shapes. These dissimilarities are used in shape matching and clustering by using hierarchical clustering algorithm with Ward's linkage rules. The presented algorithm is invariant to the affine transformation and to the boundary starting point variation. The algorithm is also capable of finding and clustering similar shapes even if there are small deformations between their boundaries.

One of the most common forms of visual communication is that of written words and line diagrams. These words and diagrams take many forms ranging from written script in English, Arabic, or other languages to symbolic diagrams like electrical circuit schematics and process flow diagrams. These scripts and diagrams can be easily digitized using a scanner, or camera, but only exist in image format. Many researchers have developed techniques to accurately recognize clean isolated symbols such as individual characters or symbols in a line diagram. However, all these systems face difficulty when two or more symbols are connected. One approach, that will work for segmenting a variety of scripts and diagram types into clean isolated symbols is presented here. This technique is based on a genetic, or evolutionary algorithms, and uses an independent and replaceable system to verify valid symbols in any connected string of characters or line diagrams. (Abstract shortened by UMI.)

This paper presents a novel rule-based system for thinning. The unique feature that distinguishes our thinning system is that it thins symbols to their central lines. This means that the shape of the symbol is preserved. It also means that the method is rotation invariant. The system has 20 rules in its inference engine. These rules are applied simultaneously to each pixel in the image. Therefore, the system has the advantages of symmetrical thinning and speed. The results show that the system is very efficient in preserving the topology of symbols and letters written in any language.